Role : MLOps Engineer (JLT).

Location : Bangalore (Working from Office / Hybrid).

Job Description : We are seeking a hands-on AI Deployment Engineer specializing in ML Engineering, Model Deployment, Model Governance, and Model Observability. The engineer will own the complete lifecycle of Deep Learning models, LLMs, and SLMs across cloud, on-premises, hybrid, and air-gapped environments.

Scope of Work :

- Build and manage MLOps and LLMOps pipelines.

- Deploy, host, and scale Deep Learning models, LLMs, and SLMs and Inference optimisation.

- Manage end-to-end model lifecycle including versioning, deployment, rollout, rollback, and retirement.

- Host models on Databricks, Kubernetes, OpenShift, and GPU-based infrastructure.

- Implement model governance, lineage, approval workflows, and compliance controls.

- Build model monitoring, observability, tracing, logging, and drift detection capabilities.

- Optimize model performance, latency, throughput, GPU utilization, and cost.

- Support cloud, on-premises, hybrid, and air-gapped environments.

Must-Have Skills :

- 58 years in MLOps, LLMOps, ML Engineering, or AI Engineering.

- Strong Python development skills.

- Hands-on experience with Databricks and/or Azure ML.

- Experience with Deep Learning, LLMs, SLMs, RAG, and Hugging Face.

- Experience deploying models built using PyTorch and TensorFlow.

- Strong expertise in model deployment on :

1. Kubernetes

2. Databricks

3. GPU Infrastructure

- Experience with :

1. vLLM

2. Triton Inference Server

3. Ray Serve

4. SGLang

5. Databricks Model Serving

- Strong GPU knowledge including NVIDIA GPUs, CUDA, multi-GPU deployments, and inference optimization.

- Experience in Model Registry, Model Governance, Model Monitoring, Drift Detection, and AI Observability.

- Strong database knowledge (SQL Server, PostgreSQL, Oracle, MySQL, MongoDB).

- Experience with Vector Databases (Pinecone, Chroma, FAISS, Milvus, Azure AI Search).

- REST APIs, WebSockets, Streaming HTTP.

- Experience with MLflow, OpenTelemetry, LangFuse, Splunk, and Grafana/ELK.

- CI/CD using Jenkins, Azure DevOps.

- Experience across Cloud, On-Premises, Hybrid, and Air-Gapped environments.

- Experience with Auth setup like Keycloak.

Good-to-Have Skills :

- Kafka, RabbitMQ, Event Hub.

- Fine-tuning and model optimization.

- Model Governance & Security.

- Experience with Llama, Mistral, DeepSeek, Qwen, Phi, and Gemma models.